#include <chrono>
#include "paddle_api.h" // NOLINT
#include "paddle_place.h"
#include "opencv2/opencv.hpp"
#include "cls_process.h"
#include "crnn_process.h"
#include "db_post_process.h"
#include "AutoLog/auto_log/lite_autolog.h"
#include <iostream>
#include <vector>
#include <cstring>
#include <sys/socket.h>
#include <arpa/inet.h>

#define PORT 9000

using namespace paddle::lite_api; // NOLINT
using namespace std;

// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
void NeonMeanScale(const float *din, float *dout, int size,
                   const std::vector<float> mean,
                   const std::vector<float> scale)
{
  if (mean.size() != 3 || scale.size() != 3)
  {
    std::cerr << "[ERROR] mean or scale size must equal to 3" << std::endl;
    exit(1);
  }
  float32x4_t vmean0 = vdupq_n_f32(mean[0]);
  float32x4_t vmean1 = vdupq_n_f32(mean[1]);
  float32x4_t vmean2 = vdupq_n_f32(mean[2]);
  float32x4_t vscale0 = vdupq_n_f32(scale[0]);
  float32x4_t vscale1 = vdupq_n_f32(scale[1]);
  float32x4_t vscale2 = vdupq_n_f32(scale[2]);

  float *dout_c0 = dout;
  float *dout_c1 = dout + size;
  float *dout_c2 = dout + size * 2;

  int i = 0;
  for (; i < size - 3; i += 4)
  {
    float32x4x3_t vin3 = vld3q_f32(din);
    float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
    float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
    float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
    float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
    float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
    float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
    vst1q_f32(dout_c0, vs0);
    vst1q_f32(dout_c1, vs1);
    vst1q_f32(dout_c2, vs2);

    din += 12;
    dout_c0 += 4;
    dout_c1 += 4;
    dout_c2 += 4;
  }
  for (; i < size; i++)
  {
    *(dout_c0++) = (*(din++) - mean[0]) * scale[0];
    *(dout_c1++) = (*(din++) - mean[1]) * scale[1];
    *(dout_c2++) = (*(din++) - mean[2]) * scale[2];
  }
}

// resize image to a size multiple of 32 which is required by the network
cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
                     std::vector<float> &ratio_hw)
{
  int w = img.cols;
  int h = img.rows;

  float ratio = 1.f;
  int max_wh = w >= h ? w : h;
  if (max_wh > max_size_len)
  {
    if (h > w)
    {
      ratio = static_cast<float>(max_size_len) / static_cast<float>(h);
    }
    else
    {
      ratio = static_cast<float>(max_size_len) / static_cast<float>(w);
    }
  }

  int resize_h = static_cast<int>(float(h) * ratio);
  int resize_w = static_cast<int>(float(w) * ratio);
  if (resize_h % 32 == 0)
    resize_h = resize_h;
  else if (resize_h / 32 < 1 + 1e-5)
    resize_h = 32;
  else
    resize_h = (resize_h / 32 - 1) * 32;

  if (resize_w % 32 == 0)
    resize_w = resize_w;
  else if (resize_w / 32 < 1 + 1e-5)
    resize_w = 32;
  else
    resize_w = (resize_w / 32 - 1) * 32;

  cv::Mat resize_img;
  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));

  ratio_hw.push_back(static_cast<float>(resize_h) / static_cast<float>(h));
  ratio_hw.push_back(static_cast<float>(resize_w) / static_cast<float>(w));
  return resize_img;
}

cv::Mat RunClsModel(cv::Mat img, std::shared_ptr<PaddlePredictor> predictor_cls,
                    const float thresh = 0.9)
{
  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};

  cv::Mat srcimg;
  img.copyTo(srcimg);
  cv::Mat crop_img;
  img.copyTo(crop_img);
  cv::Mat resize_img;

  int index = 0;
  float wh_ratio =
      static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);

  resize_img = ClsResizeImg(crop_img);
  resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);

  const float *dimg = reinterpret_cast<const float *>(resize_img.data);

  std::unique_ptr<Tensor> input_tensor0(std::move(predictor_cls->GetInput(0)));
  input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols});
  auto *data0 = input_tensor0->mutable_data<float>();

  NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
  // Run CLS predictor
  predictor_cls->Run();

  // Get output and run postprocess
  std::unique_ptr<const Tensor> softmax_out(
      std::move(predictor_cls->GetOutput(0)));
  auto *softmax_scores = softmax_out->mutable_data<float>();
  auto softmax_out_shape = softmax_out->shape();
  float score = 0;
  int label = 0;
  for (int i = 0; i < softmax_out_shape[1]; i++)
  {
    if (softmax_scores[i] > score)
    {
      score = softmax_scores[i];
      label = i;
    }
  }
  if (label % 2 == 1 && score > thresh)
  {
    cv::rotate(srcimg, srcimg, 1);
  }
  return srcimg;
}

void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
                 std::shared_ptr<PaddlePredictor> predictor_crnn,
                 std::vector<std::string> &rec_text,
                 std::vector<float> &rec_text_score,
                 std::vector<std::string> charactor_dict,
                 std::shared_ptr<PaddlePredictor> predictor_cls,
                 int use_direction_classify,
                 std::vector<double> *times,
                 int rec_image_height)
{
  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};

  cv::Mat srcimg;
  img.copyTo(srcimg);
  cv::Mat crop_img;
  cv::Mat resize_img;

  int index = 0;

  std::vector<double> time_info = {0, 0, 0};
  for (int i = boxes.size() - 1; i >= 0; i--)
  {
    auto preprocess_start = std::chrono::steady_clock::now();
    crop_img = GetRotateCropImage(srcimg, boxes[i]);
    if (use_direction_classify >= 1)
    {
      crop_img = RunClsModel(crop_img, predictor_cls);
    }
    float wh_ratio =
        static_cast<float>(crop_img.cols) / static_cast<float>(crop_img.rows);

    resize_img = CrnnResizeImg(crop_img, wh_ratio, rec_image_height);
    resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);

    const float *dimg = reinterpret_cast<const float *>(resize_img.data);

    std::unique_ptr<Tensor> input_tensor0(
        std::move(predictor_crnn->GetInput(0)));
    input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols});
    auto *data0 = input_tensor0->mutable_data<float>();

    NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
    auto preprocess_end = std::chrono::steady_clock::now();
    //// Run CRNN predictor
    auto inference_start = std::chrono::steady_clock::now();
    predictor_crnn->Run();

    // Get output and run postprocess
    std::unique_ptr<const Tensor> output_tensor0(
        std::move(predictor_crnn->GetOutput(0)));
    auto *predict_batch = output_tensor0->data<float>();
    auto predict_shape = output_tensor0->shape();
    auto inference_end = std::chrono::steady_clock::now();

    // ctc decode
    auto postprocess_start = std::chrono::steady_clock::now();
    std::string str_res;
    int argmax_idx;
    int last_index = 0;
    float score = 0.f;
    int count = 0;
    float max_value = 0.0f;

    for (int n = 0; n < predict_shape[1]; n++)
    {
      argmax_idx = int(Argmax(&predict_batch[n * predict_shape[2]],
                              &predict_batch[(n + 1) * predict_shape[2]]));
      max_value =
          float(*std::max_element(&predict_batch[n * predict_shape[2]],
                                  &predict_batch[(n + 1) * predict_shape[2]]));
      if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index)))
      {
        score += max_value;
        count += 1;
        str_res += charactor_dict[argmax_idx];
      }
      last_index = argmax_idx;
    }
    score /= count;
    rec_text.push_back(str_res);
    rec_text_score.push_back(score);
    auto postprocess_end = std::chrono::steady_clock::now();

    std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
    time_info[0] += double(preprocess_diff.count() * 1000);
    std::chrono::duration<float> inference_diff = inference_end - inference_start;
    time_info[1] += double(inference_diff.count() * 1000);
    std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
    time_info[2] += double(postprocess_diff.count() * 1000);
  }

  times->push_back(time_info[0]);
  times->push_back(time_info[1]);
  times->push_back(time_info[2]);
}

std::vector<std::vector<std::vector<int>>>
RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
            std::map<std::string, double> Config, std::vector<double> *times)
{
  // Read img
  int max_side_len = int(Config["max_side_len"]);
  int det_db_use_dilate = int(Config["det_db_use_dilate"]);

  cv::Mat srcimg;
  img.copyTo(srcimg);

  auto preprocess_start = std::chrono::steady_clock::now();
  std::vector<float> ratio_hw;
  img = DetResizeImg(img, max_side_len, ratio_hw);
  cv::Mat img_fp;
  img.convertTo(img_fp, CV_32FC3, 1.0 / 255.f);

  // Prepare input data from image
  std::unique_ptr<Tensor> input_tensor0(std::move(predictor->GetInput(0)));
  input_tensor0->Resize({1, 3, img_fp.rows, img_fp.cols});
  auto *data0 = input_tensor0->mutable_data<float>();

  std::vector<float> mean = {0.485f, 0.456f, 0.406f};
  std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
  const float *dimg = reinterpret_cast<const float *>(img_fp.data);
  NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
  auto preprocess_end = std::chrono::steady_clock::now();

  // Run predictor
  auto inference_start = std::chrono::steady_clock::now();
  predictor->Run();

  // Get output and post process
  std::unique_ptr<const Tensor> output_tensor(
      std::move(predictor->GetOutput(0)));
  auto *outptr = output_tensor->data<float>();
  auto shape_out = output_tensor->shape();
  auto inference_end = std::chrono::steady_clock::now();

  // Save output
  auto postprocess_start = std::chrono::steady_clock::now();
  float pred[shape_out[2] * shape_out[3]];
  unsigned char cbuf[shape_out[2] * shape_out[3]];

  for (int i = 0; i < int(shape_out[2] * shape_out[3]); i++)
  {
    pred[i] = static_cast<float>(outptr[i]);
    cbuf[i] = static_cast<unsigned char>((outptr[i]) * 255);
  }

  cv::Mat cbuf_map(shape_out[2], shape_out[3], CV_8UC1,
                   reinterpret_cast<unsigned char *>(cbuf));
  cv::Mat pred_map(shape_out[2], shape_out[3], CV_32F,
                   reinterpret_cast<float *>(pred));

  const double threshold = double(Config["det_db_thresh"]) * 255;
  const double max_value = 255;
  cv::Mat bit_map;
  cv::threshold(cbuf_map, bit_map, threshold, max_value, cv::THRESH_BINARY);
  if (det_db_use_dilate == 1)
  {
    cv::Mat dilation_map;
    cv::Mat dila_ele =
        cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
    cv::dilate(bit_map, dilation_map, dila_ele);
    bit_map = dilation_map;
  }
  auto boxes = BoxesFromBitmap(pred_map, bit_map, Config);

  std::vector<std::vector<std::vector<int>>> filter_boxes =
      FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg);
  auto postprocess_end = std::chrono::steady_clock::now();

  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
  times->push_back(double(preprocess_diff.count() * 1000));
  std::chrono::duration<float> inference_diff = inference_end - inference_start;
  times->push_back(double(inference_diff.count() * 1000));
  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
  times->push_back(double(postprocess_diff.count() * 1000));

  return filter_boxes;
}

std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, int num_threads)
{
  MobileConfig config;
  config.set_model_from_file(model_file);

  config.set_threads(num_threads);
  std::shared_ptr<PaddlePredictor> predictor =
      CreatePaddlePredictor<MobileConfig>(config);
  return predictor;
}

cv::Mat Visualization(cv::Mat srcimg,
                      std::vector<std::vector<std::vector<int>>> boxes)
{
  cv::Point rook_points[boxes.size()][4];
  for (int n = 0; n < boxes.size(); n++)
  {
    for (int m = 0; m < boxes[0].size(); m++)
    {
      rook_points[n][m] = cv::Point(static_cast<int>(boxes[n][m][0]),
                                    static_cast<int>(boxes[n][m][1]));
    }
  }
  cv::Mat img_vis;
  srcimg.copyTo(img_vis);
  for (int n = 0; n < boxes.size(); n++)
  {
    const cv::Point *ppt[1] = {rook_points[n]};
    int npt[] = {4};
    cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
  }

  cv::imwrite("./vis.jpg", img_vis);
  std::cout << "The detection visualized image saved in ./vis.jpg" << std::endl;
  return img_vis;
}

std::vector<std::string> split(const std::string &str,
                               const std::string &delim)
{
  std::vector<std::string> res;
  if ("" == str)
    return res;
  char *strs = new char[str.length() + 1];
  std::strcpy(strs, str.c_str());

  char *d = new char[delim.length() + 1];
  std::strcpy(d, delim.c_str());

  char *p = std::strtok(strs, d);
  while (p)
  {
    string s = p;
    res.push_back(s);
    p = std::strtok(NULL, d);
  }

  return res;
}

std::map<std::string, double> LoadConfigTxt(std::string config_path)
{
  auto config = ReadDict(config_path);

  std::map<std::string, double> dict;
  for (int i = 0; i < config.size(); i++)
  {
    std::vector<std::string> res = split(config[i], " ");
    dict[res[0]] = stod(res[1]);
  }
  return dict;
}

void check_params(int argc, char **argv)
{
  if (argc <= 1 || (strcmp(argv[1], "det") != 0 && strcmp(argv[1], "rec") != 0 && strcmp(argv[1], "system") != 0))
  {
    std::cerr << "Please choose one mode of [det, rec, system] !" << std::endl;
    exit(1);
  }
  if (strcmp(argv[1], "det") == 0)
  {
    if (argc < 9)
    {
      std::cerr << "[ERROR] usage:" << argv[0]
                << " det det_model runtime_device num_threads batchsize img_dir det_config lite_benchmark_value" << std::endl;
      exit(1);
    }
  }

  if (strcmp(argv[1], "rec") == 0)
  {
    if (argc < 9)
    {
      std::cerr << "[ERROR] usage:" << argv[0]
                << " rec rec_model runtime_device num_threads batchsize img_dir key_txt lite_benchmark_value" << std::endl;
      exit(1);
    }
  }

  if (strcmp(argv[1], "system") == 0)
  {
    if (argc < 12)
    {
      std::cerr << "[ERROR] usage:" << argv[0]
                << " system det_model rec_model clas_model runtime_device num_threads batchsize img_dir det_config key_txt lite_benchmark_value" << std::endl;
      exit(1);
    }
  }
}

void load_model_det()
{
}

vector<string> system(shared_ptr<PaddlePredictor> det, shared_ptr<PaddlePredictor> rec)
{
  // std::string det_model_file = "ch_PP-OCRv3_det_infer.nb";
  // std::string rec_model_file = "ch_PP-OCRv3_rec_infer.nb";
  // std::string cls_model_file = argv[4];
  // std::string runtime_device = "arm8";
  // std::string precision = "INT8";
  // std::string num_threads = "10";
  // std::string batchsize = "1";
  // std::string img_dir = "11.jpg";
  std::string det_config_path = "config.txt";
  std::string dict_path = "ppocr_keys_v1.txt";

  // if (strcmp(argv[6], "FP32") != 0 && strcmp(argv[6], "INT8") != 0) {
  //     std::cerr << "Only support FP32 or INT8." << std::endl;
  //     exit(1);
  // }

  // std::vector<cv::String> cv_all_img_names;
  // cv::glob(img_dir, cv_all_img_names);

  //// load config from txt file
  auto Config = LoadConfigTxt(det_config_path);
  int use_direction_classify = int(Config["use_direction_classify"]);
  int rec_image_height = int(Config["rec_image_height"]);
  auto charactor_dict = ReadDict(dict_path);
  charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
  charactor_dict.push_back(" ");

  // auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));
  // auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
  std::shared_ptr<PaddlePredictor> cls_predictor;
  // auto cls_predictor = loadModel(cls_model_file, std::stoi(num_threads));

  std::vector<double> det_time_info = {0, 0, 0};
  std::vector<double> rec_time_info = {0, 0, 0};

  // for (int i = 0; i < cv_all_img_names.size(); ++i) {
  // std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
  cv::VideoCapture cap(0);
  if (!cap.isOpened())
    std::cout << "Camera not opened" << std::endl;
  cv::Mat srcimg;
  cap >> srcimg;
  // cv::Mat srcimg = cv::imread("11.jpg");

  // if (!srcimg.data) {
  //   std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl;
  //   exit(1);
  // }

  std::vector<double> det_times;
  auto boxes = RunDetModel(det, srcimg, Config, &det_times);

  std::vector<std::string> rec_text;
  std::vector<float> rec_text_score;

  std::vector<double> rec_times;
  // RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
  //             charactor_dict, cls_predictor, use_direction_classify, &rec_times, rec_image_height);
  RunRecModel(boxes, srcimg, rec, rec_text, rec_text_score,
              charactor_dict, cls_predictor, 0, &rec_times, rec_image_height);

  //// visualization
  auto img_vis = Visualization(srcimg, boxes);

  //// print recognized text
  for (int i = 0; i < rec_text.size(); i++)
  {
    // std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
    //           <<  std::endl;
    std::cout << rec_text[i] << std::endl;

    // }

    // det_time_info[0] += det_times[0];
    // det_time_info[1] += det_times[1];
    // det_time_info[2] += det_times[2];
    // rec_time_info[0] += rec_times[0];
    // rec_time_info[1] += rec_times[1];
    // rec_time_info[2] += rec_times[2];
  }
  return rec_text;

  // if (strcmp(argv[12], "True") == 0) {
  //   AutoLogger autolog_det(det_model_file,
  //                      runtime_device,
  //                      std::stoi(num_threads),
  //                      std::stoi(batchsize),
  //                      "dynamic",
  //                      precision,
  //                      det_time_info,
  //                      cv_all_img_names.size());
  //   AutoLogger autolog_rec(rec_model_file,
  //                      runtime_device,
  //                      std::stoi(num_threads),
  //                      std::stoi(batchsize),
  //                      "dynamic",
  //                      precision,
  //                      rec_time_info,
  //                      cv_all_img_names.size());

  //   autolog_det.report();
  //   std::cout << std::endl;
  //   autolog_rec.report();
  // }
}

void det(int argc, char **argv)
{
  std::string det_model_file = argv[2];
  std::string runtime_device = argv[3];
  std::string precision = argv[4];
  std::string num_threads = argv[5];
  std::string batchsize = argv[6];
  std::string img_dir = argv[7];
  std::string det_config_path = argv[8];

  if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0)
  {
    std::cerr << "Only support FP32 or INT8." << std::endl;
    exit(1);
  }

  std::vector<cv::String> cv_all_img_names;
  cv::glob(img_dir, cv_all_img_names);

  //// load config from txt file
  auto Config = LoadConfigTxt(det_config_path);

  auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));

  std::vector<double> time_info = {0, 0, 0};
  for (int i = 0; i < cv_all_img_names.size(); ++i)
  {
    std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);

    if (!srcimg.data)
    {
      std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl;
      exit(1);
    }

    std::vector<double> times;
    auto boxes = RunDetModel(det_predictor, srcimg, Config, &times);

    //// visualization
    auto img_vis = Visualization(srcimg, boxes);
    std::cout << boxes.size() << " bboxes have detected:" << std::endl;

    for (int i = 0; i < boxes.size(); i++)
    {
      std::cout << "The " << i << " box:" << std::endl;
      for (int j = 0; j < 4; j++)
      {
        for (int k = 0; k < 2; k++)
        {
          std::cout << boxes[i][j][k] << "\t";
        }
      }
      std::cout << std::endl;
    }
    time_info[0] += times[0];
    time_info[1] += times[1];
    time_info[2] += times[2];
  }

  if (strcmp(argv[9], "True") == 0)
  {
    AutoLogger autolog(det_model_file,
                       runtime_device,
                       std::stoi(num_threads),
                       std::stoi(batchsize),
                       "dynamic",
                       precision,
                       time_info,
                       cv_all_img_names.size());
    autolog.report();
  }
}

void rec(int argc, char **argv)
{
  std::string rec_model_file = argv[2];
  std::string runtime_device = argv[3];
  std::string precision = argv[4];
  std::string num_threads = argv[5];
  std::string batchsize = argv[6];
  std::string img_dir = argv[7];
  std::string dict_path = argv[8];
  std::string config_path = argv[9];

  if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0)
  {
    std::cerr << "Only support FP32 or INT8." << std::endl;
    exit(1);
  }

  auto Config = LoadConfigTxt(config_path);
  int rec_image_height = int(Config["rec_image_height"]);

  std::vector<cv::String> cv_all_img_names;
  cv::glob(img_dir, cv_all_img_names);

  auto charactor_dict = ReadDict(dict_path);
  charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
  charactor_dict.push_back(" ");

  auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));

  std::shared_ptr<PaddlePredictor> cls_predictor;

  std::vector<double> time_info = {0, 0, 0};
  for (int i = 0; i < cv_all_img_names.size(); ++i)
  {
    std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);

    if (!srcimg.data)
    {
      std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl;
      exit(1);
    }

    int width = srcimg.cols;
    int height = srcimg.rows;
    std::vector<int> upper_left = {0, 0};
    std::vector<int> upper_right = {width, 0};
    std::vector<int> lower_right = {width, height};
    std::vector<int> lower_left = {0, height};
    std::vector<std::vector<int>> box = {upper_left, upper_right, lower_right, lower_left};
    std::vector<std::vector<std::vector<int>>> boxes = {box};

    std::vector<std::string> rec_text;
    std::vector<float> rec_text_score;
    std::vector<double> times;
    RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
                charactor_dict, cls_predictor, 0, &times, rec_image_height);

    //// print recognized text
    for (int i = 0; i < rec_text.size(); i++)
    {
      std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
                << std::endl;
    }
    time_info[0] += times[0];
    time_info[1] += times[1];
    time_info[2] += times[2];
  }
  // TODO: support autolog
  if (strcmp(argv[9], "True") == 0)
  {
    AutoLogger autolog(rec_model_file,
                       runtime_device,
                       std::stoi(num_threads),
                       std::stoi(batchsize),
                       "dynamic",
                       precision,
                       time_info,
                       cv_all_img_names.size());
    autolog.report();
  }
}

int main(int argc, char **argv)
{

  auto det_predictor = loadModel("ch_PP-OCRv3_det_infer.nb", 10);
  auto rec_predictor = loadModel("ch_PP-OCRv3_rec_infer.nb", 10);
  vector<string> message;

  // check_params(argc, argv);
  message = system(det_predictor, rec_predictor);
  // 创建客户端套接字
  int client_fd = socket(AF_INET, SOCK_STREAM, 0);
  if (client_fd == -1)
  {
    std::cerr << "Failed to create socket\n";
    exit(EXIT_FAILURE);
  }

  // 准备地址
  sockaddr_in server_addr{};
  server_addr.sin_family = AF_INET;
  server_addr.sin_addr.s_addr = inet_addr("127.0.0.1");
  server_addr.sin_port = htons(PORT);

  // 连接服务器
  if (connect(client_fd, (sockaddr *)&server_addr, sizeof(server_addr)) < 0)
  {
    std::cerr << "Failed to connect to server\n";
    exit(EXIT_FAILURE);
  }

  // 准备参数
  // std::vector<std::string> params = {"hello", "world", "from", "client"};
  std::string mes;
  for (const auto &param : message)
  {
    mes += param + '\n';
  }

  // 发送参数
  if (send(client_fd, mes.c_str(), mes.size(), 0) < 0)
  {
    std::cerr << "Failed to send message\n";
    exit(EXIT_FAILURE);
  }
  // if (strcmp(argv[1], "system") == 0) {
  //   system(argv);
  // }

  // if (strcmp(argv[1], "det") == 0) {
  //   det(argc, argv);
  // }

  // if (strcmp(argv[1], "rec") == 0) {
  //   rec(argc, argv);
  // }

  return 0;
}
